\name{calcPhenotype}
\alias{calcPhenotype}
\title{Calculates phenotype from microarray data.}
\usage{
calcPhenotype(trainingExprData, trainingPtype, testExprData,
  batchCorrect = "eb", powerTransformPhenotype = TRUE,
  removeLowVaryingGenes = 0.2, minNumSamples = 10, selection = -1,
  printOutput = TRUE)
}
\arguments{
  \item{trainingExprData}{The training data. A matrix of
  expression levels, rows contain genes and columns contain
  samples, "rownames()" must be specified and must contain
  the same type of gene ids as "testExprData"}

  \item{trainingPtype}{The known phenotype for
  "trainingExprData". A numeric vector which MUST be the
  same length as the number of columns of
  "trainingExprData".}

  \item{testExprData}{The test data where the phenotype
  will be estimted. It is a matrix of expression levels,
  rows contain genes and columns contain samples,
  "rownames()" must be specified and must contain the same
  type of gene ids as "trainingExprData".}

  \item{batchCorrect}{How should training and test data
  matrices be homogenized. Choices are "eb" (default) for
  ComBat, "qn" for quantiles normalization or "none" for no
  homogenization.}

  \item{powerTransformPhenotype}{Should the phenotype be
  power transformed before we fit the regression model?
  Default to TRUE, set to FALSE if the phenotype is already
  known to be highly normal.}

  \item{removeLowVaryingGenes}{What proportion of low
  varying genes should be removed? 20 percent be default}

  \item{minNumSamples}{How many training and test samples
  are requried. Print an error if below this threshold}

  \item{selection}{How should duplicate gene ids be
  handled. Default is -1 which asks the user. 1 to
  summarize by their or 2 to disguard all duplicates.}

  \item{printOutput}{Set to FALSE to supress output}
}
\value{
A vector of the estimated phenotype, in the same order as
the columns of "testExprData".
}
\description{
This function uses ridge regression to calculate a
phenotype from an gene expression, given a gene expression
matrix where the phenotype is already known. The function
also integrates the two datasets using a user-defined
procedure, power transforms the known phenotype and
provides several other options for flexible and powerful
prediction from a gene expression matrix.
}
\keyword{phenotype}
\keyword{predict,}

